Three things you didn’t know you could do in R: WAIAI on-demand workshop

Introduction

What is R?

R is a programming language and environment often used for statistical analysis and data visualization.

How do I use R?

Download R software: R is a free, open-source software that can be installed directly from the R project.

Download RStudio: RStudio IDE is available via Posit.

Create new R files: open R Script, R Markdown, or Quarto (or other) file types by clicking “File” > “New File” > “R Script,” “R Markdown,” or “Quarto Document”.

  • Code in R basics:

    • Objects are values saved for future use and can be created using the <- assignment operator, e.g., pi <- 3.1459 or name <- “Wharton AI and Analytics Initiative”.

    • Data-frames are tables similar to Excel spreadsheets, where each row represents an observation, each column represents a variable, and each cell represents a value.

    • Variables can take different types in R. A few common data types you might encounter in R are:

      • character for text
      • numeric (with decimals) or integer (whole numbers) for numbers
      • factor for categorical variables
      • logical for boolean values (TRUE or FALSE)
      • NA or NaN for missing values

    • Functions are commands used to act on your data with underlying code to complete specific tasks in R similar to formulas in Excel, e.g., mean(c(1, 2, 3, 4)). You can write your own functions or use pre-packaged ones from R packages.

    • Access pre-packaged R functions: additional R packages beyond base R for more useful functions are available via the Comprehensive R Archive Network, or CRAN.

    • Comments are notes written in code following a # symbol

What can I do in R?

A couple of useful examples (among many R capabilities!):

  1. Real-time access to public, openly-accessible data with web scraping

  2. Create instant, interactive web reports

  3. Automatically generate and update PowerPoint decks

Load packages

#load packages with comment on their uses
library(tidyverse) #data organization
library(rvest) #scraping
library(polite) #scraping
library(skimr) #EDA
library(psych) #EDA
library(ggpubr) #EDA
library(plotly) #EDA
library(officer) #Microsoft Office functions
library(MetBrewer) #palettes
library(rmarkdown) #Rmd formatting
library(knitr) #Rmd formatting

# save a color palette
MetBrew_Cross <- MetBrewer::met.brewer("Cross", n = 50)

## install packages by running install.packages("PACKAGE-NAME")
## cite packages by running citation(package = "PACKAGE-NAME")
## load packages by running library(PACKAGE-NAME)
## specify function from a specific package by running PACKAGE-NAME::function()
## troubleshoot functions from packages by running ?PACKAGE-NAME::function()

1: Real-time access to public, openly-accessible data with web scraping

Why it matters: With R, you can continuously monitor and utilize publicly-available, openly-accessible data with minimal manual effort, enabling rapid strategic responses to help keep your organization agile and informed.

How it works: By using the R polite and rvest packages, you can easily introduce yourself to the host website, access its rules, and scrape public website data (as long as it is permitted by the host). This can be useful for easily pulling in public, business-relevant data for analyses.

In our example, we will looking at GDP and population data for the top 50 largest Metropolitan Statistical Areas (MSAs) in the United States from Wikipedia (Wikipedia Contributors, 2025), which is available at the following URL: https://en.wikipedia.org/wiki/List_of_United_States_metropolitan_areas_by_GDP under a CC-BY-SA 4.0 license.

Note: Some websites restrict scraping access (e.g., only allow it for certain uses) or prohibit it altogether. Before embarking on any web-scraping project, be sure to thoroughly review and adhere to all ethical (e.g., privacy protections) and legal obligations and comply with websites’ Terms of Use. Also, be mindful of your scraping requests so as to not overload websites’ servers.

#save URL and introduce to host
MSA_GDP_url <- "https://en.wikipedia.org/wiki/List_of_United_States_metropolitan_areas_by_GDP" #save URL for website of interest
MSA_GDP_url_bow <- polite::bow(MSA_GDP_url) #introduce user to host website 
MSA_GDP_url_bow #check website robots.txt file for web scraping policies; also read Terms of Use on website
## <polite session> https://en.wikipedia.org/wiki/List_of_United_States_metropolitan_areas_by_GDP
##     User-agent: polite R package
##     robots.txt: 464 rules are defined for 34 bots
##    Crawl delay: 5 sec
##   The path is scrapable for this user-agent
#scrape table into dataframe
MSA_GDP_table <- 
  polite::scrape(MSA_GDP_url_bow) %>%  #scrape website from polite::bow object
  rvest::html_element("table.wikitable") %>% #choose item to be pulled from page
  rvest::html_table() %>% #convert html table to dataframe format
  base::as.data.frame() #update from tibble to dataframe format
knitr::kable(head(MSA_GDP_table, n = 10)) #see first 10 rows of raw dataframe
2023
rank Metropolitan area State(s) 2023 2022 2021 2018 2017 2016 2015 2014 2013 2012 Population (2020)[4]
1 New York-Newark-Jersey City, NY-NJ-PA (Metropolitan Statistical Area) NY, NJ, PA 2,298,868 2,163,209 1,992,779 1,772,319 1,698,122 1,634,671 1,577,366 1,511,763 1,439,043 1,401,233 20,140,470
2 Los Angeles-Long Beach-Anaheim, CA (Metropolitan Statistical Area) CA 1,295,361 1,227,469 1,124,682 1,047,661 995,114 945,600 912,384 858,170 820,353 788,081 13,200,998
3 Chicago-Naperville-Elgin, IL-IN-WI (Metropolitan Statistical Area) IL, IN, WI 894,862 832,900 764,583 689,464 659,855 641,589 627,033 599,805 577,948 561,016 9,618,502
4 San Francisco-Oakland-Berkeley, CA (Metropolitan Statistical Area) CA 778,878 729,105 668,677 548,613 509,382 469,472 446,344 413,519 383,254 364,594 4,749,008
5 Dallas-Fort Worth-Arlington, TX (Metropolitan Statistical Area) TX 744,654 688,928 598,333 512,509 482,218 458,973 442,879 420,929 394,178 375,065 7,637,387
6 Washington-Arlington-Alexandria, DC-VA-MD-WV (Metropolitan Statistical Area) DC, MD, VA, WV 714,685 660,626 607,628 540,684 515,553 500,084 481,861 460,254 448,268 442,224 6,385,162
7 Houston-The Woodlands-Sugar Land, TX (Metropolitan Statistical Area) TX 696,999 633,185 537,066 478,778 447,521 430,444 446,486 430,726 423,766 404,431 7,122,240
8 Boston-Cambridge-Newton, MA-NH (Metropolitan Statistical Area) MA, NH 610,486 571,666 531,671 463,570 439,144 421,783 406,675 381,353 365,670 357,087 4,941,632
9 Atlanta-Sandy Springs-Roswell, GA (Metropolitan Statistical Area) GA 570,663 525,888 473,823 397,261 385,542 369,806 347,604 326,502 307,750 291,481 6,089,815
10 Seattle-Tacoma-Bellevue, WA (Metropolitan Statistical Area) WA 566,742 517,803 479,966 392,036 356,572 334,411 317,153 296,028 280,291 267,472 4,018,762 
#light data cleaning
MSA_GDP_table <- MSA_GDP_table %>%
  dplyr::rename("Rank" = "2023\nrank", 
                "2020_Population" = "Population (2020)[4]",
                "MSA" = "Metropolitan area",
                "States" = "State(s)") %>% #rename some awkward column names
  dplyr::mutate(across(c(starts_with("20")), ~gsub(",", "", .))) %>% #remove commas from GDP and population numbers
  dplyr::mutate(across(MSA, ~str_trim(str_split_fixed(., ",", 2)[,1]))) %>% #remove repeated string 
  dplyr::mutate(across(c(4:14), as.numeric)) #change population and GDP to numeric

#make sure data overview makes sense
utils::str(MSA_GDP_table) #data overview and types
## 'data.frame':    50 obs. of  14 variables:
##  $ Rank           : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ MSA            : chr  "New York-Newark-Jersey City" "Los Angeles-Long Beach-Anaheim" "Chicago-Naperville-Elgin" "San Francisco-Oakland-Berkeley" ...
##  $ States         : chr  "NY, NJ, PA" "CA" "IL, IN, WI" "CA" ...
##  $ 2023           : num  2298868 1295361 894862 778878 744654 ...
##  $ 2022           : num  2163209 1227469 832900 729105 688928 ...
##  $ 2021           : num  1992779 1124682 764583 668677 598333 ...
##  $ 2018           : num  1772319 1047661 689464 548613 512509 ...
##  $ 2017           : num  1698122 995114 659855 509382 482218 ...
##  $ 2016           : num  1634671 945600 641589 469472 458973 ...
##  $ 2015           : num  1577366 912384 627033 446344 442879 ...
##  $ 2014           : num  1511763 858170 599805 413519 420929 ...
##  $ 2013           : num  1439043 820353 577948 383254 394178 ...
##  $ 2012           : num  1401233 788081 561016 364594 375065 ...
##  $ 2020_Population: num  20140470 13200998 9618502 4749008 7637387 ...
base::nrow(MSA_GDP_table) #number of rows
## [1] 50
base::names(MSA_GDP_table) #column names
##  [1] "Rank"            "MSA"             "States"          "2023"           
##  [5] "2022"            "2021"            "2018"            "2017"           
##  [9] "2016"            "2015"            "2014"            "2013"           
## [13] "2012"            "2020_Population"
knitr::kable(head(MSA_GDP_table, n = 10)) #see first 10 rows of cleaned dataframe
Rank MSA States 2023 2022 2021 2018 2017 2016 2015 2014 2013 2012 2020_Population
1 New York-Newark-Jersey City NY, NJ, PA 2298868 2163209 1992779 1772319 1698122 1634671 1577366 1511763 1439043 1401233 20140470
2 Los Angeles-Long Beach-Anaheim CA 1295361 1227469 1124682 1047661 995114 945600 912384 858170 820353 788081 13200998
3 Chicago-Naperville-Elgin IL, IN, WI 894862 832900 764583 689464 659855 641589 627033 599805 577948 561016 9618502
4 San Francisco-Oakland-Berkeley CA 778878 729105 668677 548613 509382 469472 446344 413519 383254 364594 4749008
5 Dallas-Fort Worth-Arlington TX 744654 688928 598333 512509 482218 458973 442879 420929 394178 375065 7637387
6 Washington-Arlington-Alexandria DC, MD, VA, WV 714685 660626 607628 540684 515553 500084 481861 460254 448268 442224 6385162
7 Houston-The Woodlands-Sugar Land TX 696999 633185 537066 478778 447521 430444 446486 430726 423766 404431 7122240
8 Boston-Cambridge-Newton MA, NH 610486 571666 531671 463570 439144 421783 406675 381353 365670 357087 4941632
9 Atlanta-Sandy Springs-Roswell GA 570663 525888 473823 397261 385542 369806 347604 326502 307750 291481 6089815
10 Seattle-Tacoma-Bellevue WA 566742 517803 479966 392036 356572 334411 317153 296028 280291 267472 4018762

2: Create instant, interactive reports

Why it matters: Using R Markdown or Quarto, you can rapidly generate sophisticated, interactive reports that impress stakeholders, investors, and internal teams, without any additional resources. By integrating plots and data directly into reports from R, you can also minimize the risk of error, easily update reports, and facilitate reproducibility.

How it works: We will discuss a few concepts that are helpful to create and customize your reports, and lots more information available at R Markdown: The Definitive Guide (Xie, Allaire, & Grolemund, 2023) or Quarto documents guide (from Posit).

  • YAML metadata: At the very top of an R Markdown or Quarto document and specifies cross-document information (e.g., title), settings (e.g., table of contents), and customization (e.g., visual theme).

  • Markdown text: Explanatory text written outside of code chunks in R Markdown or Quarto reports can be easily added and customized.

  • Code: R code chunks written in-line in reports are created with {r} #code chunk and they can be labeled and have chunk-specific behavior specified within the curly brackets.

  • Create report: Render your report by clicking the “knit” button, and customize knit settings using the drop-down.

Integrate descriptive statistics tables

GDP descriptive statistics per top 50 U.S. Metropolitan Statistical Area via Wikipedia

skim_type skim_variable MSA n_missing complete_rate numeric.mean numeric.sd numeric.p0 numeric.p25 numeric.p50 numeric.p75 numeric.p100 numeric.hist
numeric GDP Omaha 1 0.9 70592.44 11613.853 58032 63233.00 65867.0 76991.00 92356 ▇▇▂▂▂
numeric GDP Louisville/Jefferson County 0 1.0 71615.60 14546.578 54813 61473.50 68312.5 80172.75 97751 ▇▇▂▂▅
numeric GDP Memphis 0 1.0 77402.40 13341.510 65186 67512.00 72188.5 84057.00 102934 ▇▃▂▁▃
numeric GDP Oklahoma City 0 1.0 78089.40 12199.421 63356 69761.50 73568.5 85250.50 100054 ▇▇▂▂▅
numeric GDP New Orleans-Metairie 0 1.0 81884.40 9164.938 72811 76816.00 78555.0 81443.50 102437 ▇▅▁▂▂
numeric GDP Richmond 0 1.0 84443.70 15276.246 67572 73336.50 81123.5 91659.25 116960 ▇▆▂▂▂
numeric GDP Raleigh 0 1.0 85059.00 26192.653 57615 66562.25 75973.5 102132.25 133081 ▇▆▁▂▃
numeric GDP Jacksonville 0 1.0 85168.30 23110.713 61519 68203.25 77688.5 96821.75 129095 ▇▆▂▁▃
numeric GDP Providence-Warwick 0 1.0 86243.80 14159.734 71042 75590.50 82093.0 94538.25 111840 ▇▃▂▂▃
numeric GDP Bridgeport-Stamford-Norwalk 0 1.0 91152.70 11555.168 81498 83076.75 86164.0 96410.00 116031 ▇▁▁▁▁
numeric GDP Milwaukee-Waukesha-West Allis 0 1.0 96605.60 15613.576 87394 88642.75 89083.0 92418.00 130857 ▇▁▁▁▁
numeric GDP Salt Lake City 0 1.0 96624.70 27562.002 70114 76571.50 86455.5 112447.00 147519 ▇▃▁▂▃
numeric GDP Virginia Beach-Norfolk-Newport News 0 1.0 100007.60 13943.982 85485 90247.25 95588.0 105544.25 127459 ▇▆▂▂▂
numeric GDP Hartford-East Hartford-Middletown 0 1.0 101149.50 10352.398 92699 93803.25 96980.0 104746.50 122805 ▇▁▁▁▁
numeric GDP Las Vegas-Henderson-Paradise 0 1.0 119247.70 30657.011 87098 96240.00 109870.0 132754.25 178388 ▇▆▂▁▃
numeric GDP San Antonio-New Braunfels 0 1.0 128905.70 27698.475 93330 111489.75 122914.5 141696.25 182139 ▇▇▅▂▂
numeric GDP Cleveland-Elyria 0 1.0 131085.30 18919.419 106751 119715.25 126920.0 137287.25 173134 ▇▇▅▂▂
numeric GDP Columbus 0 1.0 131534.30 27448.808 104000 111965.50 121719.0 148213.75 182087 ▇▃▁▂▃
numeric GDP Kansas City 0 1.0 134950.90 26269.478 107028 117520.00 125621.0 148921.75 185746 ▇▆▁▃▂
numeric GDP Nashville-Davidson—Murfreesboro—Franklin 0 1.0 135195.60 37423.427 96274 109484.50 122272.5 155323.75 204861 ▇▆▁▂▃
numeric GDP Sacramento—Roseville—Arden-Arcade 0 1.0 139047.10 29141.410 101242 118803.50 133913.5 156776.25 189624 ▇▅▅▂▅
numeric GDP Indianapolis-Carmel-Anderson 0 1.0 142838.00 29480.594 111780 124158.25 132066.5 156737.00 199198 ▇▆▂▁▃
numeric GDP Orlando-Kissimmee-Sanford 0 1.0 143533.10 37361.047 103535 117245.25 130224.0 160196.00 217038 ▇▆▂▁▃
numeric GDP Cincinnati 0 1.0 145420.70 29427.984 115420 124413.75 134811.0 164063.25 198889 ▇▆▁▂▃
numeric GDP Pittsburgh 0 1.0 150152.50 23973.371 122439 134439.25 142655.5 164225.75 194230 ▇▃▂▂▃
numeric GDP Austin-Round Rock 0 1.0 153263.30 50125.403 103513 120201.75 132508.0 182025.75 248110 ▇▃▁▂▃
numeric GDP Portland-Vancouver-Hillsboro 0 1.0 156146.80 36875.343 115405 124085.00 150555.0 181032.25 218894 ▇▃▂▂▃
numeric GDP Tampa-St. Petersburg-Clearwater 0 1.0 161375.10 42584.891 117711 129864.50 147205.0 182781.50 243268 ▇▆▂▁▃
numeric GDP St. Louis 0 1.0 170745.40 28370.647 142184 151844.50 159498.5 183136.50 226549 ▇▃▂▁▃
numeric GDP Charlotte-Concord-Gastonia 0 1.0 173845.60 42388.148 131934 144594.25 158086.5 198365.00 255666 ▇▃▁▃▂
numeric GDP Riverside-San Bernardino-Ontario 0 1.0 182304.40 41710.580 132596 151960.75 172618.5 206664.50 256859 ▇▇▂▂▅
numeric GDP Baltimore-Columbia-Towson 0 1.0 199041.70 32803.477 162776 174528.50 189517.0 218553.50 259690 ▇▃▂▂▃
numeric GDP Denver-Aurora-Lakewood 0 1.0 219351.90 49242.626 167491 187883.50 202968.0 243588.50 311876 ▇▆▂▁▃
numeric GDP San Diego-Carlsbad 0 1.0 238261.50 42433.472 189074 206638.00 227404.0 262264.25 314943 ▇▃▂▂▃
numeric GDP Detroit-Warren-Dearborn 0 1.0 260671.30 37503.591 215614 232585.00 255521.0 279677.75 331333 ▇▇▅▂▂
numeric GDP Phoenix-Mesa-Scottsdale 0 1.0 263214.60 71082.514 195630 212589.50 236981.0 300870.25 398129 ▇▃▂▁▃
numeric GDP Minneapolis-St. Paul-Bloomington 0 1.0 265508.60 45004.358 213855 234825.25 255241.0 288649.25 350710 ▆▇▁▂▃
numeric GDP San Jose-Sunnyvale-Santa Clara 0 1.0 292019.50 93304.442 180996 217475.50 264596.5 385389.75 422817 ▇▇▁▂▇
numeric GDP Miami-Fort Lauderdale-West Palm Beach 0 1.0 360767.50 90404.744 266563 298137.00 337833.0 401545.25 533674 ▇▆▂▁▃
numeric GDP Seattle-Tacoma-Bellevue 0 1.0 380847.40 105528.213 267472 301309.25 345491.5 457983.50 566742 ▇▃▂▂▃
numeric GDP Atlanta-Sandy Springs-Roswell 0 1.0 399632.00 94271.520 291481 331777.50 377674.0 454682.50 570663 ▆▇▁▂▃
numeric GDP Philadelphia-Camden-Wilmington 0 1.0 437052.80 63231.474 364052 393117.00 419324.5 469222.00 557601 ▇▇▅▂▂
numeric GDP Boston-Cambridge-Newton 0 1.0 454910.50 88455.533 357087 387683.50 430463.5 514645.75 610486 ▇▃▂▂▃
numeric GDP Houston-The Woodlands-Sugar Land 0 1.0 492940.20 98923.359 404431 430514.50 447003.5 522494.00 696999 ▇▁▁▁▁
numeric GDP Dallas-Fort Worth-Arlington 0 1.0 511866.60 125737.185 375065 426416.50 470595.5 576877.00 744654 ▇▆▁▂▃
numeric GDP San Francisco-Oakland-Berkeley 0 1.0 531183.80 147080.939 364594 421725.25 489427.0 638661.00 778878 ▇▃▂▂▃
numeric GDP Washington-Arlington-Alexandria 0 1.0 537186.70 93965.576 442224 465655.75 507818.5 590892.00 714685 ▇▆▁▂▃
numeric GDP Chicago-Naperville-Elgin 0 1.0 684905.50 111672.672 561016 606612.00 650722.0 745803.25 894862 ▇▆▁▂▃
numeric GDP Los Angeles-Long Beach-Anaheim 0 1.0 1001487.50 171507.552 788081 871723.50 970357.0 1105426.75 1295361 ▇▅▅▂▅
numeric GDP New York-Newark-Jersey City 0 1.0 1748937.30 307735.527 1401233 1528163.75 1666396.5 1937664.00 2298868 ▇▃▂▂▃

Population descriptive statistics across top 50 U.S. Metropolitan Statistical Areas

vars n mean sd median trimmed mad min max range skew kurtosis se
X1 1 50 3650713 3389683 2455120 2978284 1534909 957419 20140470 19183051 2.841426 9.811452 479373.6

Integrate plots

GDP over time for top 50 U.S. Metropolitan Statistical Areas line plot

make it interactive!

GDP and Population for top 50 U.S. Metropolitan Statistical Areas scatterplot

make it interactive!

3: Automatically generate and update PowerPoint decks

Why it matters: Similar to creating reports in R, creating slide decks with R code saves manual effort, enhances reproducibility, and reduces the risk of errors, inaccuracies, or outdated information in key presentations.

How it works: The officer R package enables easy creation of slides integrating visualizations and tables directly from R code.

Note: You can also create PowerPoint decks by setting up Quarto or R Markdown files with a PowerPoint output directly (stay tuned for more information in another WAIAI tutorial)!

#open powerpoint deck
deck <- officer::read_pptx(path = "./template.pptx") %>% officer::remove_slide(index = 1) #create new powerpoint using template saved in this folder and remove blank first slide
deck <- officer::layout_default(deck, "Title and Content") #update default slide type

#add title slide
deck <- officer::add_slide(deck, layout = "Title Slide") #add new slide to open deck with title layout
deck <- officer::ph_with(deck, "U.S. Metropolitan Statistical Area GDP over time", location = ph_location_type(type = "ctrTitle")) #add and center title text
deck <- officer::ph_with(deck, "By Ginny Ulichney", location = ph_location_type(type = "subTitle")) #add and center subtitle text

#clean up table to be added to slide, create top 10 table
GDP_top10_table <- GDP_table %>% 
  dplyr::slice_max(numeric.mean, n = 10) %>% #choose top 10 MSAs by GDP from ascending table
  dplyr::select(c(MSA, numeric.mean, numeric.sd)) %>%  #select only columns we want in pptx
  rename("Mean" = "numeric.mean", 
          "SD" = "numeric.sd") #rename columns to look nicer in table

#add table slide
deck <- add_slide(deck) #add new slide for table 
deck <- ph_with(deck, "Top 10 MSAs by mean GDP (millions of dollars) via Wikipedia, 2012-2023", location = ph_location_type(type = "title")) #add title to table slide
deck <- ph_with(deck, value = GDP_top10_table, location = ph_location_type(type = "body")) #add table to slide; updates made to table will automatically be reflected when re-compiling powerpoint

#add plot slide 1
deck <- officer::add_slide(deck)
deck <- officer::ph_with(deck, "GDP increased over 2012-2023 for most U.S. Metropolitan Statistical Areas (top 50)", location = ph_location_type(type = "title")) #add title to first plot slide
deck <- officer::ph_with(deck, value = lineplot, location = ph_location_type(type = "body")) #add line plot from above to slide; again, any updates made to plot will automatically be reflected 

#add plot slide 2
deck <- officer::add_slide(deck)
deck <- officer::ph_with(deck, "2020 Population and 2021 GDP were positively associated across U.S. Metropolitan Statistical Areas (top 50)", location = ph_location_type(type = "title")) #add title to first plot slide
deck <- officer::ph_with(deck, value = scatterplot, location = ph_location_type(type = "body")) #add correlation scatterplot to slide

#save deck
print(deck, target = "./example_GDP_MSA_deck.pptx") #save finished deck to directory

What did we learn?

In this workshop, we discussed a few fundamental concepts in R and used R code to:

  • 1: Scrape real-time, openly-accessible data

  • 2: Create instant, interactive reports

  • 3: Automatically generate PowerPoint decks for easy updating and reproducibility

In up-coming videos, we’ll discuss some more advanced things you can do in R, such as:

  • Gather insights from unstructured text data using text analysis

  • Create interactive plots to enhance clarity in complex data visualizations

  • Generate data dashboards (and slide decks!) directly in Quarto

References & Additional Resources

References

R Core Team (2025). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.

Wikipedia contributors. (2025, June 5). List of United States metropolitan areas by GDP. In Wikipedia, The Free Encyclopedia. Retrieved 17:03, September 17, 2025, from https://en.wikipedia.org/w/index.php?title=List_of_United_States_metropolitan_areas_by_GDP&oldid=1294080196

Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen TL, Miller E, Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V, Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H (2019). “Welcome to the tidyverse.” Journal of Open Source Software, 4(43), 1686. doi:10.21105/joss.01686 https://doi.org/10.21105/joss.01686.

Wickham H (2024). rvest: Easily Harvest (Scrape) Web Pages. doi:10.32614/CRAN.package.rvest https://doi.org/10.32614/CRAN.package.rvest, R package version 1.0.4, https://CRAN.R-project.org/package=rvest.

William Revelle (2025). psych: Procedures for Psychological, Psychometric, and Personality Research. Northwestern University, Evanston, Illinois. R package version 2.5.3, https://CRAN.R-project.org/package=psych.

Kassambara A (2023). ggpubr: ‘ggplot2’ Based Publication Ready Plots. doi:10.32614/CRAN.package.ggpubr https://doi.org/10.32614/CRAN.package.ggpubr, R package version 0.6.0, https://CRAN.R-project.org/package=ggpubr.

C. Sievert. Interactive Web-Based Data Visualization with R, plotly, and shiny. Chapman and Hall/CRC Florida, 2020.

Gohel D, Moog S, Heckmann M (2025). officer: Manipulation of Microsoft Word and PowerPoint Documents. doi:10.32614/CRAN.package.officer https://doi.org/10.32614/CRAN.package.officer, R package version 0.6.10, https://CRAN.R-project.org/package=officer.

Mills BR (2022). MetBrewer: Color Palettes Inspired by Works at the Metropolitan Museum of Art. doi:10.32614/CRAN.package.MetBrewer https://doi.org/10.32614/CRAN.package.MetBrewer, R package version 0.2.0, https://CRAN.R-project.org/package=MetBrewer.

Allaire J, Xie Y, Dervieux C, McPherson J, Luraschi J, Ushey K, Atkins A, Wickham H, Cheng J, Chang W, Iannone R (2024). rmarkdown: Dynamic Documents for R. R package version 2.29, https://github.com/rstudio/rmarkdown.

Xie Y, Allaire J, Grolemund G (2018). R Markdown: The Definitive Guide. Chapman and Hall/CRC, Boca Raton, Florida. ISBN 9781138359338, https://bookdown.org/yihui/rmarkdown.

Xie Y, Dervieux C, Riederer E (2020). R Markdown Cookbook. Chapman and Hall/CRC, Boca Raton, Florida. ISBN 9780367563837, https://bookdown.org/yihui/rmarkdown-cookbook.

Xie Y (2025). knitr: A General-Purpose Package for Dynamic Report Generation in R. R package version 1.50, https://yihui.org/knitr/.

Yihui Xie (2015) Dynamic Documents with R and knitr. 2nd edition. Chapman and Hall/CRC. ISBN 978-1498716963

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Perepolkin D (2023). polite: Be Nice on the Web. doi:10.32614/CRAN.package.polite https://doi.org/10.32614/CRAN.package.polite, R package version 0.1.3, https://CRAN.R-project.org/package=polite

Posit team (2025). RStudio: Integrated Development Environment for R. Posit Software, PBC, Boston, MA. URL http://www.posit.co/.

## ─ Session info ───────────────────────────────────────────────────────────────
##  setting  value
##  version  R version 4.5.0 (2025-04-11)
##  os       macOS Sequoia 15.6
##  system   aarch64, darwin20
##  ui       X11
##  language (EN)
##  collate  en_US.UTF-8
##  ctype    en_US.UTF-8
##  tz       America/New_York
##  date     2025-12-17
##  pandoc   3.4 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/aarch64/ (via rmarkdown)
##  quarto   1.6.42 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/quarto
## 
## ─ Packages ───────────────────────────────────────────────────────────────────
##  package      * version date (UTC) lib source
##  abind          1.4-8   2024-09-12 [1] CRAN (R 4.5.0)
##  askpass        1.2.1   2024-10-04 [1] CRAN (R 4.5.0)
##  assertthat     0.2.1   2019-03-21 [1] CRAN (R 4.5.0)
##  backports      1.5.0   2024-05-23 [1] CRAN (R 4.5.0)
##  base64enc      0.1-3   2015-07-28 [1] CRAN (R 4.5.0)
##  broom          1.0.8   2025-03-28 [1] CRAN (R 4.5.0)
##  bslib          0.9.0   2025-01-30 [1] CRAN (R 4.5.0)
##  cachem         1.1.0   2024-05-16 [1] CRAN (R 4.5.0)
##  car            3.1-3   2024-09-27 [1] CRAN (R 4.5.0)
##  carData        3.0-5   2022-01-06 [1] CRAN (R 4.5.0)
##  cli            3.6.5   2025-04-23 [1] CRAN (R 4.5.0)
##  crosstalk      1.2.1   2023-11-23 [1] CRAN (R 4.5.0)
##  curl           6.2.2   2025-03-24 [1] CRAN (R 4.5.0)
##  data.table     1.17.2  2025-05-12 [1] CRAN (R 4.5.0)
##  dichromat      2.0-0.1 2022-05-02 [1] CRAN (R 4.5.0)
##  digest         0.6.37  2024-08-19 [1] CRAN (R 4.5.0)
##  dplyr        * 1.1.4   2023-11-17 [1] CRAN (R 4.5.0)
##  evaluate       1.0.3   2025-01-10 [1] CRAN (R 4.5.0)
##  farver         2.1.2   2024-05-13 [1] CRAN (R 4.5.0)
##  fastmap        1.2.0   2024-05-15 [1] CRAN (R 4.5.0)
##  forcats      * 1.0.0   2023-01-29 [1] CRAN (R 4.5.0)
##  Formula        1.2-5   2023-02-24 [1] CRAN (R 4.5.0)
##  fs             1.6.6   2025-04-12 [1] CRAN (R 4.5.0)
##  generics       0.1.4   2025-05-09 [1] CRAN (R 4.5.0)
##  ggplot2      * 4.0.0   2025-09-11 [1] CRAN (R 4.5.0)
##  ggpubr       * 0.6.0   2023-02-10 [1] CRAN (R 4.5.0)
##  ggsignif       0.6.4   2022-10-13 [1] CRAN (R 4.5.0)
##  glue           1.8.0   2024-09-30 [1] CRAN (R 4.5.0)
##  gtable         0.3.6   2024-10-25 [1] CRAN (R 4.5.0)
##  hms            1.1.3   2023-03-21 [1] CRAN (R 4.5.0)
##  htmltools      0.5.8.1 2024-04-04 [1] CRAN (R 4.5.0)
##  htmlwidgets    1.6.4   2023-12-06 [1] CRAN (R 4.5.0)
##  httr           1.4.7   2023-08-15 [1] CRAN (R 4.5.0)
##  jquerylib      0.1.4   2021-04-26 [1] CRAN (R 4.5.0)
##  jsonlite       2.0.0   2025-03-27 [1] CRAN (R 4.5.0)
##  knitr        * 1.50    2025-03-16 [1] CRAN (R 4.5.0)
##  labeling       0.4.3   2023-08-29 [1] CRAN (R 4.5.0)
##  lattice        0.22-6  2024-03-20 [1] CRAN (R 4.5.0)
##  lazyeval       0.2.2   2019-03-15 [1] CRAN (R 4.5.0)
##  lifecycle      1.0.4   2023-11-07 [1] CRAN (R 4.5.0)
##  lubridate    * 1.9.4   2024-12-08 [1] CRAN (R 4.5.0)
##  magrittr       2.0.3   2022-03-30 [1] CRAN (R 4.5.0)
##  Matrix         1.7-3   2025-03-11 [1] CRAN (R 4.5.0)
##  memoise        2.0.1   2021-11-26 [1] CRAN (R 4.5.0)
##  MetBrewer    * 0.2.0   2022-03-21 [1] CRAN (R 4.5.0)
##  mgcv           1.9-1   2023-12-21 [1] CRAN (R 4.5.0)
##  mime           0.13    2025-03-17 [1] CRAN (R 4.5.0)
##  mnormt         2.1.1   2022-09-26 [1] CRAN (R 4.5.0)
##  nlme           3.1-168 2025-03-31 [1] CRAN (R 4.5.0)
##  officer      * 0.6.10  2025-05-30 [1] CRAN (R 4.5.0)
##  openssl        2.3.2   2025-02-03 [1] CRAN (R 4.5.0)
##  pillar         1.11.0  2025-07-04 [1] CRAN (R 4.5.0)
##  pkgconfig      2.0.3   2019-09-22 [1] CRAN (R 4.5.0)
##  plotly       * 4.11.0  2025-06-19 [1] CRAN (R 4.5.0)
##  polite       * 0.1.3   2023-06-30 [1] CRAN (R 4.5.0)
##  psych        * 2.5.3   2025-03-21 [1] CRAN (R 4.5.0)
##  purrr        * 1.0.4   2025-02-05 [1] CRAN (R 4.5.0)
##  R6             2.6.1   2025-02-15 [1] CRAN (R 4.5.0)
##  ragg           1.4.0   2025-04-10 [1] CRAN (R 4.5.0)
##  ratelimitr     0.4.1   2018-10-07 [1] CRAN (R 4.5.0)
##  RColorBrewer   1.1-3   2022-04-03 [1] CRAN (R 4.5.0)
##  Rcpp           1.0.14  2025-01-12 [1] CRAN (R 4.5.0)
##  readr        * 2.1.5   2024-01-10 [1] CRAN (R 4.5.0)
##  repr           1.1.7   2024-03-22 [1] CRAN (R 4.5.0)
##  rlang          1.1.6   2025-04-11 [1] CRAN (R 4.5.0)
##  rmarkdown    * 2.29    2024-11-04 [1] CRAN (R 4.5.0)
##  robotstxt      0.7.15  2024-08-29 [1] CRAN (R 4.5.0)
##  rstatix        0.7.2   2023-02-01 [1] CRAN (R 4.5.0)
##  rstudioapi     0.17.1  2024-10-22 [1] CRAN (R 4.5.0)
##  rvest        * 1.0.4   2024-02-12 [1] CRAN (R 4.5.0)
##  S7             0.2.0   2024-11-07 [1] CRAN (R 4.5.0)
##  sass           0.4.10  2025-04-11 [1] CRAN (R 4.5.0)
##  scales         1.4.0   2025-04-24 [1] CRAN (R 4.5.0)
##  selectr        0.4-2   2019-11-20 [1] CRAN (R 4.5.0)
##  sessioninfo    1.2.3   2025-02-05 [1] CRAN (R 4.5.0)
##  skimr        * 2.1.5   2022-12-23 [1] CRAN (R 4.5.0)
##  spiderbar      0.2.5   2023-02-11 [1] CRAN (R 4.5.0)
##  stringi        1.8.7   2025-03-27 [1] CRAN (R 4.5.0)
##  stringr      * 1.5.1   2023-11-14 [1] CRAN (R 4.5.0)
##  systemfonts    1.2.3   2025-04-30 [1] CRAN (R 4.5.0)
##  textshaping    1.0.1   2025-05-01 [1] CRAN (R 4.5.0)
##  tibble       * 3.3.0   2025-06-08 [1] CRAN (R 4.5.0)
##  tidyr        * 1.3.1   2024-01-24 [1] CRAN (R 4.5.0)
##  tidyselect     1.2.1   2024-03-11 [1] CRAN (R 4.5.0)
##  tidyverse    * 2.0.0   2023-02-22 [1] CRAN (R 4.5.0)
##  timechange     0.3.0   2024-01-18 [1] CRAN (R 4.5.0)
##  tzdb           0.5.0   2025-03-15 [1] CRAN (R 4.5.0)
##  usethis        3.1.0   2024-11-26 [1] CRAN (R 4.5.0)
##  uuid           1.2-1   2024-07-29 [1] CRAN (R 4.5.0)
##  vctrs          0.6.5   2023-12-01 [1] CRAN (R 4.5.0)
##  viridisLite    0.4.2   2023-05-02 [1] CRAN (R 4.5.0)
##  withr          3.0.2   2024-10-28 [1] CRAN (R 4.5.0)
##  xfun           0.52    2025-04-02 [1] CRAN (R 4.5.0)
##  xml2           1.3.8   2025-03-14 [1] CRAN (R 4.5.0)
##  yaml           2.3.10  2024-07-26 [1] CRAN (R 4.5.0)
##  zip            2.3.3   2025-05-13 [1] CRAN (R 4.5.0)
## 
##  [1] /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/library
##  * ── Packages attached to the search path.
## 
## ──────────────────────────────────────────────────────────────────────────────